A generalized vector space model for text retrieval based on semantic relatedness

We introduce a new information retrieval model, the generalized vector space model (GVSM). In the GVSM, Boolean-like queries can be transformed into vectors spanned by the atoms of the free Boolean algebra generated by the index terms. Documents can, therefore, be ranked with respect to a query as in the conventional vector space model. Most significantly, the GVSM provides a unified view of the Boolean retrieval and vector-processing systems.

QuantNet is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under quantlet. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.

All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to WordNet’s synsets cooperating with WordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in WordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed concepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD.

Semantic relatedness means the degree of the nearness of two documents or two terms based on the sameness of their meaning or semantic contents. A method for computing semantic relatedness is crucial in the web mining applications such as search engine, recommendation systems and information retrieval systems, and also in the area of natural language processing. This paper proposes a new technique for measuring semantic relatedness between two terms. It uses Wikipedia as a source of structured world knowledge about the terms of interest while ontologies are used to retrieve the semantically related information in most systems. The proposed method used spreading activation approach over the taxonomy of Wikipedia categories to get the measures of semantic relatedness. Spreading activation strategy is a popular approach in associative retrieval. The proposed method is experienced with the benchmark dataset to show the comparison our approach with some existing approaches.

An improved focused crawler based on Semantic Similarity Vector Space Model

This paper presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to literally and regularly express a concept. We apply set algebra to Wordnet’s synsets cooperating with Wordnet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a using Boolean operators and various word forms in synset(s). Then we construct a formal mechanism for quantifying and estimating the semantic relatedness between concepts - we facilitate concept distribution statistics to determine the degree of semantic relatedness between two lexically expressed concepts. Then we applied the measure of semantic relatedness to the WSD task. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for word sense disambiguation.

This paper presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to Wordnet's synsets cooperating with Wordnet's word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in Wordnet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts – we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed concepts. Human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “Word Sense Disambiguation (WSD)”. The proposed method is not supervised, and does not require any manually created sense‐tagged training examples. The experimental results showed good performance on Semcor, a subset of the Brown corpus. We observe that measures of semantic relatedness are useful sources of information for word sense disambiguation.

This paper presents a cross-modal approach for extracting semantic relationships between concepts using tagged images. In the proposed method, we first project both text and visual features of the tagged images to a latent space using canonical correlation analysis (CCA). Then, under the probabilistic interpretation of CCA, we calculate a representative distribution of the latent variables for each concept. Based on the representative distributions of the concepts, we derive two types of measures: the semantic relatedness between the concepts and the abstraction level of each concept. Because these measures are derived from a cross-modal scheme that enables the collaborative use of both text and visual features, the semantic relationships can successfully reflect semantic and visual contexts. Experiments conducted on tagged images collected from Flickr show that our measures are more coherent to human cognition than the conventional measures that use either text or visual features, or the WordNet-based measures. In particular, a new measure of semantic relatedness, which satisfies the triangle inequality, obtains the best results among different distance measures in our framework. The applicability of our measures to multimedia-related tasks such as concept clustering, image annotation and tag recommendation is also shown in the experiments.

Department of PsychologyUniversity of PennsylvaniaInvited chapter for LSA: A Road Toward Meaning. In press September 8, 2005AbstractIn remembering a list of words, subjects’ order of recall can reveal the in-fluence of both semantic and temporal associations among items. In thischapter, we examine how well measures of semantic relatedness (e.g., Lan-dauer & Dumais, 1997; Steyvers, Shiffrin, & Nelson, 2004) predict the orderof subject’s recalls. Analysis of recall transitions reveal that subtle varia-tions in semantic relatedness strongly influence memory retrieval. Contraryto the view that temporal and semantic similarity strictly compete as re-trieval cues, the data reveal that these two factors are separately modifiable,at least under certain conditions. These findings are not easily reconciledwithin current models of episodic and semantic memory.A central function of episodic memory is to form and utilize associations betweenitems experienced at nearby times. In addition to these newly–formed episodic associations,subjects enter the laboratory with a great deal of knowledge about verbal stimuli. Studyingthe relation between episodic and pre–existing, or semantic, associations can help shedlight on the processes that lead to episodic retrieval. One prominent view is that episodicmemory and semantic memory are separate memory systems (Tulving, 1983, 2002), and

Numerous measures have been proposed to determine the semantic relatedness between words. Earlier approaches rely on the location of the words within the structure of a terminological ontology and are categorized into distance-based or information content models. More recently research has considered the overlapping of attributes or relationships, i.e., feature matching, between words or lexical concepts. This paper focuses on Tversky's prominent parameterized ratio model and its fundamental role in semantic relatedness measures. Existing semantic relatedness measures from the distance-based and information content categories are unified through this model. Through appropriate feature selection of properties of word objects in terminological ontologies, the model may also be used as the basis for proposing numerous other approaches to creating a variety of semantic relatedness measures

A measure of semantic relatedness for resolving ambiguities in natural language database requests

Existing evaluation methods either investigate whether ontologies are “fit for purpose”, or focus on evaluating consistencies of the data in regard to the axioms defined in the ontologies. In this paper, we focus on how to evaluate the consistency of ontology with respect to taxonomic relationships, and we suggest a new measure to evaluate the taxonomic consistency of an ontology. As the semantic relatedness measure between concepts in ontologies is the key part of ontology consistency measure, we firstly propose a new approach to measure the semantic rela-tedness for pairs of words. Several experiments demonstrate that the proposed ontology measure identifing the consistency with respect to taxonomic relationships is better than existing approaches.

Searching for the best sense for a polysemous word remains one of the greatest challenges in the representation of biomedical text. To this end, Word Sense Disambiguation (WSD) algorithms mostly rely on an External Source of Knowledge, like a Thesaurus or Ontology, for automatically selecting the proper concept of an ambiguous term in a given Window of Context using semantic similarity and relatedness measures. In this paper, we propose a Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. This measure uses the large volume of documents returned by PubMed Search engine to determine the greater context for a biomedical short text through a new term weighting scheme based on Rough Set Theory (RST). To illustrate the efficiency of our proposed method, we evaluate a WSD algorithm based on this measure on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. The obtained results demonstrate promising improvements.

Several approaches for computing semantic similarity and relatedness measures between terms have been developed. This paper proposes a new semantic similarity measure between two nodes concentrating on nouns as well as their hypernym/hyponym relationships based on the structure of Wordnet. In particular, the similarity of two given nouns depends not only on their positions but also on their relevancy connections in the hierarchy. We evaluate our measure and the other ones on dataset of Miller-Charles and then compute the correlation coefficients to the human judgments. The experimental results show that our method outperforms edge-counting methods.

Most service and network management applications are developed to use vendor- and device-specific management data. These data are produced from different languages, and hence can have different representations of the same concept. We define a novel knowledge representation and mapping mechanism that uses ontological concepts and relations to generate a formal description for the Directory Enabled Networks new generation information model. This endows the model with the necessary semantic richness and formalism to represent different types of information for use in network management operations. Data is extracted from models, and is used to represent facts. These facts are semantically related to concepts and relations from one or more ontologies using a lexicon. A new data structure is then built by combining knowledge extracted from model elements with knowledge extracted from ontological elements. Semantic relatedness measures are then used to associate modeled data with ontological data. Early results are also presented.